The improvements in point clouds based network involved a number of estimation processes.
Recently, FlowNet3D [1]} proposed an approach to tackle 3D scene flow expanding on the building blocks introduced in [2]}.
It provides the 3D flow for each point in a reference point cloud deducing its motion from the comparison with a second, successive, point cloud.
Estimating the scene flow is a problem closely related to the scan registration.
Intuitively, a network able to approximate such task results suitable for ICP related inferences.
In fact, the main difference between the two tasks lies in the generalization needed to shift from a high dimensional flow to the estimate of a rigid transformation in the ICP case.
We leverage the main structure of FlowNet3D to tackle the estimation of ICP errors, modifying few key aspects to adapt it to our problem.
To complete the architecture we add a final MLP layer reducing the dimensionality to the number of parameters needed to predict the Gaussian parameters.
First we follow the original regression layer from FlowNet3D reducing the feature space to \(\mathbb {R}^3 \times n\) .
The MLP is extended to take as input the feature produced by the regression layer and to return the parameters generating the covariance matrix and predicted error vector.
All the MLP (originally Linear-BatchNorm-ReLU) in FlowNet3D are stacked with Dropout modules to allow the estimation of the epistemic covariance matrix. Additionally, this proved to reduce overfitting as expected.
